A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms
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A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms
You'll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms.
A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms
Č. položky: 86456855

A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms

86% of respondents would recommend this to a friend

Č. položky: 86456855

€ 63

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You'll discover many algorithms, tools, and methods to model optimization problems with the QUBO and Ising formalisms.
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Detaily produktu

Shop A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms online at a best price in Slovakia. 1804613835
Publisher Packt Publishing
Publication date March 31, 2023
Edition 1st
Language English
Print length 680 pages
ISBN-10 1804613835
ISBN-13 978-1804613832
Item Weight 2.53 pounds (1.15 kg)
Dimensions 7.5 x 1.6 x 9.25 inches (19.1 x 4.1 x 23.5 cm)

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A Practical Guide to Quantum Machine Learning and Quantum Optimization: Hands-on Approach to Modern Quantum Algorithms

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Python Editorial Review

**** “A Practical Guide to Quantum Machine Learning and Quantum Optimization,” authored by Elías F. Combarro and Samuel González-Castillo, emerges as a significant contribution to the field of quantum computing literature, especially for those with an interest in algorithms and practical applications. Drawing from the success of a transformative online course, this book serves both novices and experts with a carefully structured pedagogical approach. Readers appreciate the authors' dedication to clarity and engagement. The book is commended for its systematic breakdown of complex topics, including an emphasis on practical algorithm implementation that can significantly aid comprehension. The unique feature of the book is its focus on modern quantum algorithms, particularly in quantum optimization and quantum machine learning, bridging theory with real-world coding through examples and exercises. Key functionalities include important study notes, supplementary resources for deeper learning, and a collection of exercises that enhance understanding. The integration of PennyLane as a programming framework stands out, granting users exposure to a flexible tool for quantum computing applications. Reviewers highlight that the coding exercises allow readers to experience quantum computing through hands-on practice, fostering a deeper understanding of fundamental concepts and their applications. The book's layout is designed for accessibility: sections can serve educational purposes independently or as a comprehensive course on advanced quantum algorithms. Many have found it indispensable as both a learning tool and a desk reference for ongoing work in the field. Readers recommend this book unequivocally for anyone looking to enhance their knowledge and skills in quantum computing and machine learning, indicating that it meets and exceeds expectations set during the initial online lectures. **

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Pros

  • Comprehensive coverage of quantum algorithms particularly in optimization and machine learning
  • Engaging and clear pedagogical style
  • Plenty of practical exercises, with answers provided
  • Use of the PennyLane programming framework, noted for inter-operability
  • Ideal for both self-study and guided courses
  • Appendices that aid in quick understanding of necessary mathematical concepts

Zápory

  • Basic concepts such as quantum teleportation and certain canonical algorithms may be excluded

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Funkcie a výhody

  • Hands-on approach to modern quantum algorithms.
  • Minimal mathematical prerequisites required.
  • Learn to solve real-world problems with practical examples.
  • Utilize programming frameworks like Qiskit and PennyLane.
  • Build a solid foundation in quantum computing fundamentals.
  • Gain skills to implement quantum machine learning models.

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